Consistency of Pseudolikelihood Estimation of Fully Visible Boltzmann Machines

Boltzmann machine is a classic model of neural computation, and a number of methods have been proposed for its estimation. Most methods are plagued by either very slow convergence, or asymptotic bias in the resulting estimates. Here we consider estimation in the basic case of fully visible Boltzmann machines. We show that the old principle of pseudolikelihood estimation provides an estimator that is computationally very simple, yet statistically consistent.

• Type: Journal
• Year: 2006
• Where: NECO
• Authors: Aapo Hyvärinen
• Keywords: Asymptotic Bias, Boltzmann Machines, Estimation, NECO 2006